Search Results (31,523)

Forest volume is an important information for assessing the economic value and carbon sequestration capacity of forest resources and serves as a key indicator for energy flow and biodiversity. Although remote sensing technology is applied to estimate volume, optical remote sensing data have limitations in capturing forest vertical height information and may suffer from reflectance saturation. While LiDAR data can provide more detailed vertical structural information, they come with high processing costs and limited observation range. Therefore, improving the accuracy of volume estimation through multi-source data fusion has become a crucial challenge and research focus in the field of forest remote sensing. In this study, we integrated Sentinel-2 multispectral data, Resource-3 stereoscopic imagery, UAV-based LiDAR data, and field survey data to quantitatively estimate the forest volume in Saihanwula Nature Reserve, located in Inner Mongolia, China, on the southern part of Daxing’anling Mountains. The study evaluated the performance of multi-source remote sensing features by using recursive feature elimination (RFE) to select the most relevant factors and applied four machine learning models—multiple linear regression (MLR), k-nearest neighbors (kNN), random forest (RF), and gradient boosting regression tree (GBRT)—to develop volume estimation models. The evaluation metrics include the coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (rRMSE). The results show that (1) forest Canopy Height Model (CHM) data were strongly correlated with forest volume, helping to alleviate the reflectance saturation issues inherent in spectral texture data. The fusion of CHM and spectral data resulted in an improved volume estimation model with R² = 0.75 and RMSE = 8.16 m³/hm², highlighting the importance of integrating multi-source canopy height information for more accurate volume estimation. (2) Volume estimation accuracy varied across different tree species. For Betula platyphylla, we obtained R² = 0.71 and RMSE = 6.96 m³/hm²; for Quercus mongolica, R² = 0.74 and RMSE = 6.90 m³/hm²; and for Populus davidiana, R² = 0.51 and RMSE = 9.29 m³/hm². The total forest volume in the Saihanwula Reserve ranges from 50 to 110 m³/hm². (3) Among the four machine learning models, GBRT consistently outperformed others in all evaluation metrics, achieving the highest R² of 0.86, lowest RMSE of 9.69 m³/hm², and lowest rRMSE of 24.57%, suggesting its potential for forest biomass estimation. In conclusion, accurate estimation of forest volume is critical for evaluating forest management practices and timber resources. While this integrated approach shows promise, its operational application requires further external validation and uncertainty analysis to support policy-relevant decisions. The integration of multi-source remote sensing data provides valuable support for forest resource accounting, economic value assessment, and monitoring dynamic changes in forest ecosystems. Full article

Hydrotreated ester and fatty acids to jet (HEFA-tJ) is presently the most developed and economically attractive pathway to produce sustainable aviation fuel (SAF). An ongoing systematic study of the critical variables of different pathways to SAF has revealed significantly lower greenhouse gas (GHG) reduction potential for the HEFA-tJ pathway compared to competing markets using the same resources for road diesel production. Moderate yield variations between air and road pathways lead to several hundred thousand tons less GHG reduction per project, which is generally not evaluated thoroughly in standard environmental assessments. This work demonstrates that, although the HEFA-tJ market seems to have more attractive features than biodiesel/renewable diesel, considerable viability risks might manifest as HEFA-tJ fuel market integration rises. The need for more transparent data and effort in this regard, before envisaging making decisions regarding the volume of HEFA-tJ production, is emphasized. Overall, reducing the carbon intensity of road diesel appears to be less capital-intensive, less risky, and several times more efficient in reducing GHG emissions. Full article

Although organizational resilience is well established, refining the systematic quantitative evaluation of health systems resilience (HSR) remains an ongoing opportunity for advancement. Research either focuses on individual HSR indicators, such as social welfare policy, public expenditure, health insurance, healthcare quality, and technology, or broadly examines socio-economic factors, highlighting the need for a more comprehensive methodological approach. This study employed the Slacks-Based Measure (SBM) within Data Envelopment Analysis (DEA) to analyze efficiency by maximizing outputs. It systematically examined key HSR factors across countries, providing insights for improved policymaking and resource allocation. Taking a five-year (2016–2020) dataset that covered 55 to 56 countries and evaluating 17 indicators across governance, health systems, and economic aspects, the paper presents that all sixteen top-ranked countries with a perfect efficiency score of 1 belonged to the high-income group, with ten in Europe, highlighting regional HSR differences. This paper concludes that adequate economic resources form the foundation of HSR and ensure stability and sustained progress. A properly supported healthcare workforce is essential for significantly enhancing health systems and delivering quality care. Last, effective governance and the equitable allocation of resources are crucial for fostering sustainable development and strengthening HSR. Full article

Amid China’s national strategic prioritization of the Chengdu–Chongqing Economic Circle and accelerated territorial spatial planning, this study deciphered the synergistic evolution of Land Use Intensity (LUI) and Balance Degree of Land Use Structure (BDLUS) during rapid urbanization. Leveraging 1 km grid units and integrating emerging spatiotemporal hotspot analysis, BFAST, and geographic detectors, we systematically analyzed spatiotemporal patterns and drivers of LUI, BDLUS, and their Coupling Coordination Degree (CCD) from 2000 to 2022. Key findings: (1) LUI strongly correlated with economic growth, with core areas reaching high-intensity development (average > 2.96) versus ecologically constrained marginal zones (<2.42), marked by abrupt changes during 2011–2014; (2) BDLUS improvements covered 82.22% of the study area, driven by the Yangtze River Economic Belt strategy (21.96% hotspot concentration), yet structural imbalance persisted in transitional zones (18.81% cold spots); (3) CCD exhibited center-edge dichotomy, contrasting high-value cores (CCD > 0.68) with ecologically sensitive edges (9.80% cold spots), peaking in regulatory shifts around 2010; (4) terrain constraints and intensified human activities (the interaction effect between nighttime lighting and population density increased by 219.49% after 2020) jointly governed coupling mechanisms, with urbanization and industrial transition becoming dominant drivers. This research advances an “intensity–structure–coordination” framework and elucidates “dual-core resonance” dynamics, offering theoretical foundations for spatial optimization and ecological civilization. Full article

Monitoring water quality is crucial for achieving clean water and sanitation goals, particularly in remote areas. The project “Morbidity vs. Water Quality for Human Consumption in Tonosí: A Pilot Study” aimed to enhance water quality assessments in Panama using advanced analytical techniques to assess volatile organic compounds, heavy metals, and microbiological pathogens. To support this, the Technical Unit for Water Quality (UTECH) was established, featuring a novel mobile laboratory with cutting-edge technology for accurate testing, minimal chemical reagent use, reduced waste generation, and equipped with a solar-powered battery system. The aim of this paper is to explore the design, deployment, and impact of the UTECH. Furthermore, this study presents results from three sampling points in Tonosí, where several parameters exceeded regulatory limits, demonstrating the capabilities of the UTECH and highlighting the need for ongoing monitoring and intervention. The study also assesses the environmental, social, and economic impacts of the UTECH in alignment with the Sustainable Development Goals and national initiatives. Finally, a SWOT analysis illustrates the UTECH’s potential to improve water quality assessments in Panama while identifying areas for sustainable growth. The study showcases the successful integration of advanced mobile laboratory technologies into water quality monitoring, contributing to sustainable development in Panama and offering a replicable model for similar initiatives in other regions. Full article

Detection of soil pollution by petroleum products is necessary to remedy threats to economic and human health. Pollution by hydraulic oil often occurs through leaks from forestry machinery such as harvesters. Electronic noses equipped with gas sensor arrays are promising tools for applications of pollution detection and monitoring. A self-made, low-cost electronic nose was used for differentiation between clean and polluted samples, with two types of oils and three levels of pollution severity. An electronic nose uses the TGS series of gas sensors, manufactured by Figaro Inc. Sensor responses to changes in environmental conditions from clean air to measured odor, as well as responses to changes in sensor operation temperature, were used for analysis. Statistically significant response results allowed for the detection of pollution by biodegradable oil, while standard mineral oil was difficult to detect. It was demonstrated that the TGS 2602 gas sensor is most suitable for the studied application. LDA analysis demonstrated multidimensional data patterns allowing differentiation between sample categories and pollution severity levels. Full article

Earthquakes, as one of the most destructive natural disasters, often cause significant casualties and severe economic losses. Accurate prediction of earthquake fatalities is of great importance for pre-disaster prevention and mitigation planning, as well as post-disaster emergency response deployment. To address the challenges of small sample sizes, high dimensionality, and strong nonlinearity in earthquake fatality prediction, this paper proposes an integrated modeling approach (PCA-IWOA-XGBoost) combining Principal Component Analysis (PCA), the Improved Whale Optimization Algorithm (IWOA), and Extreme Gradient Boosting (XGBoost). The method first employs PCA to reduce the dimensionality of the influencing factor data, eliminating redundant information and improving modeling efficiency. Subsequently, the IWOA is used to intelligently optimize key hyperparameters of the XGBoost model, enhancing the prediction accuracy and stability. Using 42 major earthquake events in China from 1970 to 2025 as a case study, covering regions including the west (e.g., Tonghai in Yunnan, Wenchuan, Jiuzhaigou), central (e.g., Lushan in Sichuan, Ya’an), east (e.g., Tangshan, Yingkou), north (e.g., Baotou in Inner Mongolia, Helinger), northwest (e.g., Jiashi in Xinjiang, Wushi, Yongdeng in Gansu), and southwest (e.g., Lancang in Yunnan, Lijiang, Ludian), the empirical results showed that the PCA-IWOA-XGBoost model achieved an average test set accuracy of 97.0%, a coefficient of determination (R²) of 0.996, a root mean square error (RMSE) and mean absolute error (MAE) reduced to 4.410 and 3.430, respectively, and a residual prediction deviation (RPD) of 21.090. These results significantly outperformed the baseline XGBoost, PCA-XGBoost, and IWOA-XGBoost models, providing improved technical support for earthquake disaster risk assessment and emergency response. Full article

Understanding the utilization patterns of nuclear medicine (NM) services is essential for optimizing resource allocation and service provision. This study aimed to address the regional evidence gap by reporting the demand for NM services by sex and age at a public hospital in Jamaica. This was a non-experimental, retrospective study of NM scans that were completed at the University Hospital of the West Indies from 1 June 2022 to 31 May 2024. While all scans were reported in the descriptive totals, for patients with multiple scans during the study period, only the data from the first visit was used in the inferential statistical analysis. This was performed with the IBM SPSS (version 29.0) software and involved the use of chi-square goodness of fit and multinomial logistic regression. A total of 1135 NM scans for 1098 patients were completed (37 patients had more than one scan); 596 (54.3%) were female and 502 (45.7%) were male, with the ages ranging from 3 days to 94 years old. Among the female patients, there was a greater demand in the ≥60 years age group for cardiac amyloid scans (χ² = 6.40, p < 0.05), while females 18–59 years had a greater demand for thyroid scans (χ² = 7.714, p < 0.05) and bone scans (χ² = 3.904, p < 0.05). On the other hand, significantly more males in the ≥60 age group presented for cardiac amyloid (χ² = 4.167; p < 0.05) and bone scans (χ² = 145.79, p < 0.01). Males were significantly less likely to undergo a thyroid scan than females (p < 0.01, OR = 0.072, 95% CI: 0.021, 0.243) while individuals aged 18–59 years were more likely to undergo this scan than patients aged 60 or older (p = 0.02, OR = 3.565, 95% CI: 1.258, 10.104). Males were more likely to do a cardiac amyloid scan (p < 0.05, OR = 2.237, 95% CI: 1.023, 4.891) but less likely to undergo a cardiac rest/stress test than females (p = 0.02, OR = 0.307, 95% CI: 0.114, 0.828). Prolonged life expectancy and an aging population have the potential to impact NM utilization, thus requiring planning for infrastructure, equipment, work force, and supplies. Cancer-related and cardiovascular indications are a top priority at this facility; hence, age- and sex-specific analysis are useful in establishing models for policy makers with regard to the allocation of economic and human resources for the sustainability of this specialized service. Full article

Heat stress in dairy cows, caused by high temperature and humidity during summer, has led to significant declines in milk production and severe economic losses for farms. Exosomes—extracellular vesicles carrying bioactive molecules—are critical for intercellular communication and immunity but remain understudied in heat-stressed Holstein cows. In this study, we extracted exosomes from three heat-stressed (HS) cows and three non-heat-stressed (Ctr) cows and employed proteomics to analyze plasma exosomes. We identified a total of 28 upregulated and 18 downregulated proteins in the HS group compared to the control group. Notably, we observed a significant upregulation of key protein groups, including cytoskeletal regulators, signaling mediators, and coagulation factors, alongside the downregulation of HP-25_1. These differentially expressed proteins demonstrate strong potential as heat stress biomarkers. GO and KEGG analyses linked the differentially expressed proteins to actin cytoskeleton regulation and endoplasmic reticulum pathways. Additionally, protein–protein interaction (PPI) analysis revealed the PI3K-Akt signaling pathway as a central node in the cellular response to heat stress. These findings establish plasma exosomes as valuable biospecimens, provide valuable insights into the molecular mechanisms of heat stress response, and may contribute to the development of precision breeding strategies for enhanced thermal resilience in dairy herds. Full article

In recent years, industrial wastewater discharge containing heavy metals has increased significantly and has adversely affected both human health and the aquatic ecosystem. The increasing demand for metals in industry has prompted researchers to focus on developing effective and economical methods for removal of these metals. In this study, the removal of Ni(II) from wastewater using the Graphene oxide@Fe₃O₄@Pluronic-F68 (GO@Fe₃O₄@Pluronic-F68) nano composite as an adsorbent was investigated. The nanocomposite was characterised using a series of analytical methods, including Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), X-ray diffraction (XRD), and Brunauer-Emmett-Teller (BET) analysis. The effects of contact time, pH, adsorbent amount, and temperature parameters on adsorption were investigated. Various adsorption isotherm models were applied to interpret the equilibrium data in aqueous solutions; the compatibility of the Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich models with experimental data was examined. For a kinetic model consistent with experimental data, pseudo-first-order, pseudo-second-order, Elovich, and intra-particle diffusion models were examined. The maximum adsorption capacity was calculated as 151.5 mg·g⁻¹ in the Langmuir isotherm model. The most suitable isotherm and kinetic models were the Freundlich and pseudo-second-order kinetic models, respectively. These results demonstrate the potential of the GO@Fe₃O₄@Pluronic-F68 nanocomposite as an adsorbent offering a sustainable solution for Ni(II) removal. Full article

Establishing an entrepreneurship education ecosystem is crucial for the continual nurturing of young entrepreneurs and, consequently, the enhancement of economic development. Beyond the expansion of entrepreneurship programs, the active involvement and support from relevant resources and external stakeholders are pivotal to constructing such ecosystems. However, obstacles arise from the lower intention of external stakeholders to participate, and constraints imposed by information technology, hindering the ecosystem’s development. The Metaverse, an innovative technology amalgamating three-dimensional virtual technologies with blockchain and artificial intelligence, emerges as a potential solution to overcome these barriers and construct an entrepreneurship education ecosystem. Despite this potential, there is a lack of analysis explaining how the Metaverse achieves this. To address this gap, a framework for entrepreneurship education ecosystems is established in this study, highlighting two barriers and elucidating how these barriers impede ecosystem construction. Furthermore, four efficiencies of the Metaverse are identified as key factors with positive effects in terms of surmounting barriers to ensure the successful establishment of an entrepreneurship education ecosystem: communication convenience, enhanced simulation environment, information filtering, and the creation of valuable information. Full article

The mining industry (MI) in mineral-rich regions is pivotal for economic growth but is challenged by significant pollution and emissions. This study examines Guangxi, a representative region in China, in light of the country’s “Dual Carbon” goals. We quantified carbon emissions from the MI from 2005 to 2021, employing the generalized Divisia index method (GDIM) to analyze the factors driving these emissions. Additionally, a system dynamics (SD) model was developed, integrating economic, demographic, energy, environmental, and policy variables to assess decarbonization strategies and the potential for carbon decoupling. The key findings include the following: (1) Carbon accounting analysis reveals a rising emission trend in Guangxi’s MI, predominantly driven by electricity consumption, with the non-ferrous metal mining sector contributing the largest share of total emissions. (2) The primary drivers of carbon emissions were identified as economic scale, population intensity, and energy intensity, with periodic fluctuations in sector-specific drivers necessitating coordinated policy adjustments. (3) Scenario analysis showed that the Emission Reduction Scenario (ERS) is the only approach that achieves a carbon peak before 2030, indicating that it is the most effective decarbonization pathway. (4) Between 2022 and 2035, carbon decoupling from total output value is projected to improve under both the Energy-Saving Scenario (ESS) and ERS, achieving strong decoupling, while the resource extraction shows limited decoupling effects often displaying an expansionary connection. This study aims to enhance the understanding and promote the advancement of green and low-carbon development within the MI in mineral-rich regions. Full article

Floods are among the most devastating hydrometeorological natural disasters worldwide, causing massive infrastructure and economic loss in low-lying, flood-prone developing countries like Bangladesh. Effective disaster mitigation relies on organized and detailed flood damage information to facilitate emergency evacuation, coordinate relief distribution, and formulate an effective disaster management policy. Nevertheless, the nation confronts considerable obstacles due to insufficient historical flood damage data and the underdevelopment of near-real-time (NRT) flood monitoring systems. This study addresses this issue by developing a replicable methodology for flood damage assessment and NRT monitoring systems. Using the Google Earth Engine (GEE) platform, we analyzed flood events from 2019 to 2023, integrating geospatial layers such as roads, cropland, etc. Analysis of flood events over the five-year period revealed substantial impacts, with 21.60% of the total area experiencing inundation. This flooding affected 6.92% of cropland and 4.16% of the population. Furthermore, 18.10% of the road network, spanning over 21,000 km within the study area, was also affected. This system has the potential to enhance emergency response capabilities during flood events and inform more effective disaster mitigation policies. Full article

Porcine circovirus type 2 (PCV2) is a globally prevalent swine pathogen that induces immunosuppression, predisposing pigs to subclinical infections. In intensive farming systems, PCV2 persistently impairs growth performance and vaccine efficacy, leading to substantial economic losses in the swine industry. Emerging evidence suggests that certain viruses exploit Suppressor of Cytokine Signaling 3 (SOCS3), a key immune checkpoint protein, to subvert host innate immunity by suppressing cytokine signaling. While SOCS3 has been implicated in various viral infections, its regulatory role in PCV2 replication remains undefined. This study aims to elucidate the mechanisms underlying the interplay between SOCS3 and PCV2 during viral pathogenesis. Porcine SOCS3 was amplified using RT-PCR and stably overexpressed in PK-15 cells through lentiviral delivery. Bioinformatics analysis facilitated the design of three siRNA candidates targeting SOCS3. We systematically investigated the effects of SOCS3 overexpression and knockdown on PCV2 replication kinetics and host antiviral responses by quantifying the viral DNA load and the mRNA levels of cytokines. PCV2 infection upregulated SOCS3 expression at both transcriptional and translational levels in PK-15 cells. Functional studies revealed that SOCS3 overexpression markedly enhanced viral replication, whereas its knockdown suppressed viral proliferation. Intriguingly, SOCS3-mediated immune modulation exhibited a divergent regulation of antiviral cytokines: PCV2-infected SOCS3-overexpressing cells showed elevated IFN-β but suppressed TNF-α expressions, whereas SOCS3 silencing conversely downregulated IFN-β while amplifying TNF-α responses. This study unveils a dual role of SOCS3 during subclinical porcine circovirus type 2 (PCV2) infection: it functions as a host-derived pro-viral factor that facilitates viral replication while simultaneously reshaping the cytokine milieu to suppress overt inflammatory responses. These findings provide novel insights into the mechanisms underlying PCV2 immune evasion and persistence and establish a theoretical framework for the development of host-targeted control strategies. Although our results identify SOCS3 as a key host determinant of PCV2 persistence, the precise molecular pathways involved require rigorous experimental validation. Full article

Skeletal muscle development is a critical economic trait in livestock, governed by myogenic satellite cell regulation. Integrins mediate mechanical anchorage to the ECM and enable ECM–intracellular signaling. CIB2, as an EF-hand-domain protein involved in mechanotransduction, shows significant developmental regulation in goat muscle. Although the role of CIB2 in skeletal muscle growth is poorly characterized, we observed pronounced developmental upregulation of IB2 in postnatal goat muscle. CIB2 expression increased >20-fold by postnatal day 90 (P90) compared to P1, sustaining elevation through P180 (p < 0.05). Functional investigations indicated that siRNA-mediated knockdown of CIB2 could inhibit myoblast proliferation by inducing S-phase arrest (p < 0.05) and downregulating the expression of CDK4/Cyclin D/E. Simultaneously, CIB2 interference treatment was found to decrease the proliferative activity of goat myogenic satellite cells, yet it significantly promoted differentiation by upregulating the expression of MyoD/MyoG/MyHC (p < 0.01). Mechanistically, CTCF was identified as a transcriptional repressor binding to an intragenic region of the CIB2 gene locus (ChIP enrichment: 2.3-fold, p < 0.05). Knockdown of CTCF induced upregulation of CIB2 (p < 0.05). RNA-seq analysis established CIB2 as a calcium signaling hub: its interference activated IL-17/TNF and complement cascades, while overexpression suppressed focal adhesion/ECM–receptor interactions and enriched neuroendocrine pathways. Collectively, this study identifies the CTCF-CIB2–integrin α7β1–PI3K/AKT axis as a novel molecular mechanism that regulates the balance of myogenic fate in goats. These findings offer promising targets for genomic selection and precision breeding strategies aimed at enhancing muscle productivity in ruminants. Full article